Project: Communicate Data Findings - [Ford GoBike System Data ]

Table of Contents

• Introduction
• Data Wrangling
• Univariate Exploratory Data Analysis and Conclusions
• Bivariate Exploratory Data Analysis and Conclusions
• Multivariate Exploratory Data Analysis and Conclusions

Introduction

Dataset Description

This data set includes information about individual rides made in a bike-sharing system covering the greater San Francisco Bay area.

Univariate Exploratory Data Analysis and Conclusions

Research Question 1: At what time of the day is the demand highest?

Conclusion.

• As we can see the highest demand is just before working hours and just after working hours. let's see if it's the same during the weekends.

Conclusion.

• Understandably, the demand is lowest in the morning and goes up as we approach midday.

Bivariate Exploratory Data Analysis and Conclusions

Research Question 3: How often is the service used by customers or subscribers?

Conclusion.

• Interesting! Customers make longer trips than subscribers. But do they make more trips?

Conclusion.

• Although customer make longer trips subscribers make way more trips.

Multivariate Exploratory Data Analysis and Conclusions

Research Question 6: Is there any correlation between age, gender and user type?

Conclusion.

• Age is irrelevant to people being customers or subscribers.
• People who select the gender other are usually older, females are usually younger.
• This Chart could be very useful for the marketing team to be able to make tageted ads based on age group to convert customers to subscribers